ASU Scholarship Showcase

The relationship between sequence and binding properties of an aptamer for immunoglobulin E (IgE) was investigated using custom DNA microarrays. Single, double and some triple mutations of the aptamer sequence were created to evaluate the importance of specific base composition on aptamer binding. The majority of the positions in the aptamer sequence were found to be immutable, with changes at these positions resulting in more than a 100-fold decrease in binding affinity. Improvements in binding were observed by altering the stem region of the aptamer, suggesting that it plays a significant role in binding. Results obtained for the various mutations were used to estimate the information content and the probability of finding a functional aptamer sequence by selection from a random library. For the IgE-binding aptamer, this probability is on the order of 10^-10 to 10^-9. Results obtained for the double and triple mutations also show that there are no compensatory mutations within the space defined by those mutations. Apparently, at least for this particular aptamer, the functional sequence space can be represented as a rugged landscape with sharp peaks defined by highly constrained base compositions. This makes the rational optimization of aptamer sequences using step-wise mutagenesis approaches very challenging.

Mutation is the ultimate source of all genetic variation and is, therefore, central to evolutionary change. Previous work on Paramecium tetraurelia found an unusually low germline base-substitution mutation rate in this ciliate. Here, we tested the generality of this result among ciliates using Tetrahymena thermophila. We sequenced the genomes of 10 lines of T. thermophila that had each undergone approximately 1,000 generations of mutation accumulation (MA). We applied an existing mutation-calling pipeline and developed a new probabilistic mutation detection approach that directly models the design of an MA experiment and accommodates the noise introduced by mismapped reads. Our probabilistic mutation-calling method provides a straightforward way of estimating the number of sites at which a mutation could have been called if one was present, providing the denominator for our mutation rate calculations. From these methods, we find that T. thermophila has a germline base-substitution mutation rate of 7.61 × 10 ^-12 per-site, per cell division, which is consistent with the low base-substitution mutation rate in P. tetraurelia. Over the course of the evolution experiment, genomic exclusion lines derived from the MA lines experienced a fitness decline that cannot be accounted for by germline base-substitution mutations alone, suggesting that other genetic or epigenetic factors must be involved. Because selection can only operate to reduce mutation rates based upon the "visible" mutational load, asexual reproduction with a transcriptionally silent germline may allow ciliates to evolve extremely low germline mutation rates.

The most common evolutionary events at the molecular level are single-base substitutions, as well as insertions and deletions (indels) of short DNA segments. A large body of research has been devoted to develop probabilistic substitution models and to infer their parameters using likelihood and Bayesian approaches. In contrast, relatively little has been done to model indel dynamics, probably due to the difficulty in writing explicit likelihood functions. Here, we contribute to the effort of modeling indel dynamics by presenting SpartaABC, an approximate Bayesian computation (ABC) approach to infer indel parameters from sequence data (either aligned or unaligned). SpartaABC circumvents the need to use an explicit likelihood function by extracting summary statistics from simulated sequences. First, summary statistics are extracted from the input sequence data. Second, SpartaABC samples indel parameters from a prior distribution and uses them to simulate sequences. Third, it computes summary statistics from the simulated sets of sequences. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC can provide an approximation to the posterior distribution of indel parameters as well as point estimates. We study the performance of our methodology and show that it provides accurate estimates of indel parameters in simulations. We next demonstrate the utility of SpartaABC by studying the impact of alignment errors on the inference of positive selection. A C ++ program implementing SpartaABC is freely available in http://spartaabc.tau.ac.il.

Butyrate is a common fatty acid produced in important fermentative systems, such as the human/animal gut and other H₂ production systems. Despite its importance, there is little information on the partnerships between butyrate producers and other bacteria. The objective of this work was to uncover butyrate-producing microbial communities and possible metabolic routes in a controlled fermentation system aimed at butyrate production. The butyrogenic reactor was operated at 37°C and pH 5.5 with a hydraulic retention time of 31 h and a low hydrogen partial pressure (PH₂). High-throughput sequencing and metagenome functional prediction from 16S rRNA data showed that butyrate production pathways and microbial communities were different during batch (closed) and continuous-mode operation. Lactobacillaceae, Lachnospiraceae, and Enterococcaceae were the most abundant phylotypes in the closed system without PH₂ control, whereas Prevotellaceae, Ruminococcaceae, and Actinomycetaceae were the most abundant phylotypes under continuous operation at low PH₂. Putative butyrate producers identified in our system were from Prevotellaceae, Clostridiaceae, Ruminococcaceae, and Lactobacillaceae. Metagenome prediction analysis suggests that nonbutyrogenic microorganisms influenced butyrate production by generating butyrate precursors such as acetate, lactate, and succinate. 16S rRNA gene analysis suggested that, in the reactor, a partnership between identified butyrogenic microorganisms and succinate (i.e., Actinomycetaceae), acetate (i.e., Ruminococcaceae and Actinomycetaceae), and lactate producers (i.e., Ruminococcaceae and Lactobacillaceae) took place under continuous-flow operation at low PH₂.

Anode-respiring bacteria (ARB) generate electric current in microbial electrochemical cells (MXCs) by channeling electrons from the oxidation of organic substrates to an electrode. Production of high current densities by monocultures in MXCs has resulted almost exclusively from the activity of Geobacter sulfurreducens, a neutrophilic freshwater Fe(III)-reducing bacterium and the highest-current-producing member documented for the Geobacteraceae family of the Deltaproteobacteria. Here we report high current densities generated by haloalkaliphilic Geoalkalibacter spp., thus broadening the capability for high anode respiration rates by including other genera within the Geobacteraceae. In this study, acetate-fed pure cultures of two related Geoalkalibacter spp. produced current densities of 5.0 to 8.3 and 2.4 to 3.3 A m^-2 under alkaline (pH 9.3) and saline (1.7% NaCl) conditions, respectively. Chronoamperometric studies of halophilic Glk. subterraneus DSM 23483 and alkaliphilic Glk. ferrihydriticus DSM 17813 suggested that cells performed long-range electron transfer through electrode-attached biofilms and not through soluble electron shuttles. Glk. ferrihydriticus also oxidized ethanol directly to produce current, with maximum current densities of 5.7 to 7.1 A m^-2 and coulombic efficiencies of 84 to 95%. Cyclic voltammetry (CV) elicited a sigmoidal response with characteristic onset, midpoint, and saturation potentials, while CV performed in the absence of an electron donor suggested the involvement of redox molecules in the biofilm that were limited by diffusion. These results matched those previously reported for actively respiring Gb. sulfurreducens biofilms producing similar current densities (~5 to 9 A m^-2).

There is an increasing awareness that health care must move from post-symptomatic treatment to presymptomatic intervention. An ideal system would allow regular inexpensive monitoring of health status using circulating antibodies to report on health fluctuations. Recently, we demonstrated that peptide microarrays can do this through antibody signatures (immunosignatures). Unfortunately, printed microarrays are not scalable. Here we demonstrate a platform based on fabricating microarrays (~10 M peptides per slide, 330,000 peptides per assay) on silicon wafers using equipment common to semiconductor manufacturing. The potential of these microarrays for comprehensive health monitoring is verified through the simultaneous detection and classification of six different infectious diseases and six different cancers. Besides diagnostics, these high-density peptide chips have numerous other applications both in health care and elsewhere.

Swinging arms are a key functional component of multistep catalytic transformations in many naturally occurring multi-enzyme complexes. This arm is typically a prosthetic chemical group that is covalently attached to the enzyme complex via a flexible linker, allowing the direct transfer of substrate molecules between multiple active sites within the complex. Mimicking this method of substrate channelling outside the cellular environment requires precise control over the spatial parameters of the individual components within the assembled complex. DNA nanostructures can be used to organize functional molecules with nanoscale precision and can also provide nanomechanical control. Until now, protein–DNA assemblies have been used to organize cascades of enzymatic reactions by controlling the relative distance and orientation of enzymatic components or by facilitating the interface between enzymes/cofactors and electrode surfaces. Here, we show that a DNA nanostructure can be used to create a multi-enzyme complex in which an artificial swinging arm facilitates hydride transfer between two coupled dehydrogenases. By exploiting the programmability of DNA nanostructures, key parameters including position, stoichiometry and inter-enzyme distance can be manipulated for optimal activity.